# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

import functools
import logging
import math
from typing import Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig

from sglang.srt.distributed import (
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
    tensor_model_parallel_all_reduce,
)
from sglang.srt.layers.activation import GeluAndMul
from sglang.srt.layers.elementwise import (
    fused_dual_residual_rmsnorm,
    fused_rmsnorm,
    gelu_and_mul_triton,
)
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
from sglang.srt.layers.moe.router import fused_moe_router_shim
from sglang.srt.layers.moe.topk import TopK
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.rotary_embedding import (
    RotaryEmbedding,
    _yarn_find_correction_range,
    _yarn_get_mscale,
    get_rope,
)
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.loader import DefaultModelLoader
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.utils import add_prefix

logger = logging.getLogger(__name__)


class Grok1MLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        layer_id: int,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        reduce_results=True,
        use_presharded_weights: bool = False,
        split_gate_up: bool = False,
    ) -> None:
        super().__init__()

        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("gate_up_proj", prefix),
            use_presharded_weights=use_presharded_weights,
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=add_prefix("down_proj", prefix),
            reduce_results=reduce_results,
            use_presharded_weights=use_presharded_weights,
        )
        self.act_fn = GeluAndMul(approximate="tanh")
        self.layer_id = layer_id

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x, _ = gelu_and_mul_triton(gate_up)
        x, _ = self.down_proj(x)
        return x


class Grok1MoE(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int,
        num_experts: int,
        top_k: int,
        hidden_size: int,
        intermediate_size: int,
        params_dtype: Optional[torch.dtype] = None,
        quant_config: Optional[QuantizationConfig] = None,
        tp_size: Optional[int] = None,
        reduce_results: bool = True,
        use_presharded_weights: bool = False,
        inplace: bool = True,
        no_combine: bool = False,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = hidden_size

        self.gate = ReplicatedLinear(
            hidden_size,
            num_experts,
            bias=False,
            params_dtype=torch.float32,
            quant_config=None,
        )

        self.router_logit_softcapping = 30.0
        custom_routing_function = functools.partial(
            fused_moe_router_shim, self.router_logit_softcapping
        )

        self.topk = TopK(
            top_k=top_k,
            renormalize=False,
            custom_routing_function=custom_routing_function,
        )

        self.experts = FusedMoE(
            num_experts=num_experts,
            top_k=top_k,
            layer_id=layer_id,
            hidden_size=hidden_size,
            intermediate_size=intermediate_size,
            params_dtype=params_dtype,
            quant_config=quant_config,
            activation="gelu",
            reduce_results=reduce_results,
            use_presharded_weights=use_presharded_weights,
            inplace=inplace,
            no_combine=no_combine,
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        topk_output = self.topk(hidden_states, self.gate.weight)
        return self.experts(hidden_states, topk_output)


def _yarn_linear_ramp_mask(
    low: float, high: float, dim: int, dtype: torch.dtype
) -> torch.Tensor:
    if low == high:
        low -= 0.001  # Prevent singularity

    linear_func = (torch.arange(dim, dtype=dtype) - low) / (high - low)
    ramp_func = torch.clamp(linear_func, 0, 1)
    return ramp_func


def get_rope_scaling(config):
    rope_type = getattr(config, "rope_type", None)
    if rope_type:
        original_max_position_embeddings = getattr(
            config, "original_max_position_embeddings", None
        )
        scaling_factor = getattr(config, "scaling_factor", None)
        extrapolation_factor = getattr(config, "extrapolation_factor", 1.0)
        attn_factor = getattr(config, "attn_factor", 1.0)
        beta_fast = getattr(config, "beta_fast", 32)
        beta_slow = getattr(config, "beta_slow", 1)
        rope_scaling = {
            "extra_method": rope_type,
            "max_position_embeddings": original_max_position_embeddings,
            "scaling_factor": scaling_factor,
            "extrapolation_factor": extrapolation_factor,
            "attn_factor": attn_factor,
            "beta_fast": beta_fast,
            "beta_slow": beta_slow,
            "dtype": torch.bfloat16,
        }
        return rope_scaling
    else:
        return None


class ScalingRotaryEmbedding(RotaryEmbedding):
    """Scale the RotaryEmbedding in a way similar to YaRN method. https://arxiv.org/pdf/2309.00071."""

    def __init__(
        self,
        head_size: int,
        rotary_dim: int,
        max_position_embeddings: int,
        base: int,
        is_neox_style: bool,
        scaling_factor: float,
        dtype: torch.dtype,
        *,
        extra_method: str = "yarn_log",
        extrapolation_factor: float = 1,
        attn_factor: float = 1,
        beta_fast: int = 32,
        beta_slow: int = 1,
    ) -> None:
        self.scaling_factor = scaling_factor
        self.extra_method = extra_method
        self.extrapolation_factor = extrapolation_factor
        self.attn_factor = attn_factor
        self.beta_fast = beta_fast
        self.beta_slow = beta_slow
        # Get n-d magnitude scaling corrected for interpolation
        self.mscale = float(_yarn_get_mscale(self.scaling_factor) * attn_factor)
        super().__init__(
            head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
        )

    def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
        pos_freqs = self.base ** (
            torch.arange(0, self.rotary_dim, 2, dtype=torch.float) / self.rotary_dim
        )
        inv_freq_extrapolation = 1.0 / pos_freqs
        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)

        low, high = _yarn_find_correction_range(
            self.beta_fast,
            self.beta_slow,
            self.rotary_dim,
            self.base,
            self.max_position_embeddings,
        )
        # Get n-d rotational scaling corrected for extrapolation
        inv_freq_mask = (
            1
            - _yarn_linear_ramp_mask(low, high, self.rotary_dim // 2, dtype=torch.float)
        ) * self.extrapolation_factor
        if self.extra_method in ["original"]:
            inv_freq = inv_freq_extrapolation
        elif self.extra_method in ["yarn", "yarn_linear"]:
            inv_freq = (
                inv_freq_interpolation * (1 - inv_freq_mask)
                + inv_freq_extrapolation * inv_freq_mask
            )
        elif self.extra_method == "yarn_log":
            inv_freq = torch.exp(
                torch.log(inv_freq_extrapolation) * inv_freq_mask
                + torch.log(inv_freq_interpolation) * (1.0 - inv_freq_mask)
            )
        elif self.extra_method == "theta_scale":
            exponents = torch.arange(0, self.rotary_dim, 2, dtype=torch.float)
            theta_scale_exponent = self.base ** (
                math.log(
                    self.max_position_embeddings * self.scaling_factor / (2 * math.pi)
                )
                / math.log(self.max_position_embeddings / (2 * math.pi))
            )
            inv_freq = torch.tensor(
                1.0 / (theta_scale_exponent ** (exponents / self.rotary_dim)),
                dtype=torch.float32,
            )
        else:
            raise ValueError(f"Unknown extrapolation method: {self.extra_method}")
        return inv_freq

    def _compute_cos_sin_cache(self) -> torch.Tensor:
        inv_freq = self._compute_inv_freq(self.scaling_factor)
        t = torch.arange(
            self.max_position_embeddings * self.scaling_factor, dtype=torch.float32
        )
        freqs = torch.einsum("i,j -> ij", t, inv_freq)
        # cos = freqs.cos() * self.mscale
        # sin = freqs.sin() * self.mscale
        cos = freqs.cos()
        sin = freqs.sin()
        cache = torch.cat((cos, sin), dim=-1)
        return cache


class Grok1Attention(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        layer_id: int = 0,
        max_position: int = 4096 * 32,
        rope_theta: float = 10000,
        quant_config: Optional[QuantizationConfig] = None,
        reduce_results: bool = True,
        alt_stream: Optional[torch.cuda.Stream] = None,
        load_presharded_attn: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.layer_id = layer_id
        self.hidden_size = hidden_size
        attn_tp_rank = get_tensor_model_parallel_rank()
        attn_tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % attn_tp_size == 0
        self.num_heads = self.total_num_heads // attn_tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= attn_tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % attn_tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert attn_tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
        self.head_dim = getattr(config, "head_dim", 128)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        rope_scaling = get_rope_scaling(config)
        self.load_presharded_attn = load_presharded_attn
        self.alt_stream = alt_stream or torch.cuda.Stream()

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
            load_presharded_attn=self.load_presharded_attn,
            prefix=add_prefix("qkv_proj", prefix),
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            reduce_results=reduce_results,
            tp_rank=attn_tp_rank,
            tp_size=attn_tp_size,
            use_presharded_weights=self.load_presharded_attn,
            prefix=add_prefix("o_proj", prefix),
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position,
            base=int(self.rope_theta),
            is_neox_style=True,
        )

        self.rope_rotate_half_dims = getattr(config, "rope_rotate_half_dims", False)

        if rope_scaling is not None:
            self.rotary_emb = ScalingRotaryEmbedding(
                self.head_dim,
                rotary_dim=(
                    self.head_dim
                    if not self.rope_rotate_half_dims
                    else self.head_dim // 2
                ),
                base=int(self.rope_theta),
                is_neox_style=True,
                **rope_scaling,
            )
            pos_encoding_mode = "NONE"
        else:
            self.rotary_emb = get_rope(
                self.head_dim,
                rotary_dim=(
                    self.head_dim
                    if not self.rope_rotate_half_dims
                    else self.head_dim // 2
                ),
                max_position=max_position,
                base=int(self.rope_theta),
                is_neox_style=True,
            )
            pos_encoding_mode = "NONE"

        logit_cap = max(getattr(config, "attn_logit_softcapping", 30.0), 0.0)
        logit_capping_method = getattr(config, "attn_logit_softcapping_method", "tanh")

        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
            logit_cap=logit_cap,
            quant_config=quant_config,
            pos_encoding_mode=pos_encoding_mode,
            logit_capping_method=logit_capping_method,
            prefix=add_prefix("attn", prefix),
        )
        self.attn.xai_temperature_len = getattr(self.config, "attn_temperature_len", -1)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)

        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)

        attn_output = self.attn(q, k, v, forward_batch)

        output, _ = self.o_proj(attn_output)
        return output


class Grok1DecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        layer_id: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
        load_presharded_moe: bool = False,
        load_presharded_attn: bool = False,
        load_presharded_mlp: bool = False,
        alt_stream: Optional[torch.cuda.Stream] = None,
        skip_moe: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.num_experts = config.num_local_experts
        self.hidden_size = config.hidden_size
        self.residual_moe = getattr(config, "residual_moe", False)
        self.layer_id = layer_id
        self.alt_stream = alt_stream or torch.cuda.Stream()

        rope_theta = getattr(config, "rope_theta", 10000)
        self.self_attn = Grok1Attention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=(
                config.context_len
                if hasattr(config, "context_len")
                else config.max_position_embeddings
            ),
            num_kv_heads=config.num_key_value_heads,
            layer_id=layer_id,
            rope_theta=rope_theta,
            quant_config=quant_config,
            reduce_results=False,
            alt_stream=self.alt_stream,
            load_presharded_attn=load_presharded_attn,
            prefix=add_prefix("attn", prefix),
        )

        split_gate_up = not getattr(config, "merge_gate_up", True)
        if self.num_experts > 0:
            self.block_sparse_moe = Grok1MoE(
                config=config,
                layer_id=layer_id,
                num_experts=config.num_local_experts,
                top_k=config.num_experts_per_tok,
                hidden_size=config.hidden_size,
                intermediate_size=getattr(
                    config,
                    "moe_intermediate_size",
                    getattr(config, "intermediate_size", None),
                ),
                quant_config=quant_config,
                reduce_results=not self.residual_moe,
                use_presharded_weights=load_presharded_moe,
                inplace=False,  # not self.residual_moe,
                no_combine=False,  # self.residual_moe,  # just a suggestion to not combine topk
                prefix=add_prefix("block_sparse_moe", prefix),
            )
            if self.residual_moe:
                self.mlp = Grok1MLP(
                    hidden_size=config.hidden_size,
                    intermediate_size=config.intermediate_size,
                    quant_config=quant_config,
                    reduce_results=False,
                    use_presharded_weights=load_presharded_mlp,
                    layer_id=layer_id,
                    split_gate_up=split_gate_up,
                )
        else:
            raise NotImplementedError()

        self.pre_attn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.pre_moe_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_moe_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        if self.num_experts > 0:
            if self.residual_moe:
                # NOTE: self.block_sparse_moe modifies the input in-place,
                # so we have to call it later. Be aware of any possible related errors.
                if get_tensor_model_parallel_world_size() > 1:
                    self.ffn = lambda x: tensor_model_parallel_all_reduce(
                        self.moe_with_rmoe(x)
                    )
                else:
                    self.ffn = self.moe_with_rmoe
            else:
                self.ffn = self.block_sparse_moe
        else:
            raise NotImplementedError()

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor] = None,
        deferred_norm: Optional[RMSNorm] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, RMSNorm]:

        hidden_states_original = hidden_states
        residual_original = residual

        # Self Attention
        if deferred_norm is not None:
            assert residual is not None
            # here hidden_states is output of ffn, residual is residual from after previous attn layer
            hidden_states, residual = fused_dual_residual_rmsnorm(
                hidden_states,
                residual,
                deferred_norm.weight,
                self.pre_attn_norm.weight,
                deferred_norm.variance_epsilon,
            )
        else:
            # here hidden_states is the residual
            hidden_states, residual = (
                fused_rmsnorm(
                    hidden_states,
                    self.pre_attn_norm.weight,
                    self.pre_attn_norm.variance_epsilon,
                ),
                hidden_states,
            )

        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            forward_batch=forward_batch,
        )

        if get_tensor_model_parallel_world_size() > 1:
            hidden_states = tensor_model_parallel_all_reduce(hidden_states)

        hidden_states, residual = fused_dual_residual_rmsnorm(
            hidden_states,
            residual,
            self.post_attn_norm.weight,
            self.pre_moe_norm.weight,
            self.post_attn_norm.variance_epsilon,
        )

        # Fully Connected
        hidden_states = self.ffn(hidden_states)
        return hidden_states, residual, self.post_moe_norm  # defer layernorm

    def moe_with_rmoe(self, x):
        if self.alt_stream is not None and get_is_capture_mode():
            current_stream = torch.cuda.current_stream()
            self.alt_stream.wait_stream(current_stream)
            mlp_result = self.mlp(x)
            with torch.cuda.stream(self.alt_stream):
                moe_result = self.block_sparse_moe(x)
            current_stream.wait_stream(self.alt_stream)
        else:
            mlp_result = self.mlp(x)
            moe_result = self.block_sparse_moe(x)
        return (mlp_result + moe_result) / 1.4142135623730951


class Grok1Model(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        load_presharded_moe: bool = False,
        load_presharded_embedding: bool = False,
        load_presharded_attn: bool = False,
        load_presharded_mlp: bool = False,
        replicate_embedding: bool = False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            use_presharded_weights=load_presharded_embedding,
            enable_tp=not replicate_embedding,
            prefix=add_prefix("embed_tokens", prefix),
        )

        self.alt_stream = torch.cuda.Stream()
        self.layers = nn.ModuleList(
            [
                Grok1DecoderLayer(
                    config,
                    i,
                    quant_config=quant_config,
                    load_presharded_moe=load_presharded_moe,
                    load_presharded_attn=load_presharded_attn,
                    load_presharded_mlp=load_presharded_mlp,
                    alt_stream=self.alt_stream,
                )
                for i in range(config.num_hidden_layers)
            ]
        )
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
            hidden_states.mul_(self.config.embedding_multiplier_scale)
        else:
            hidden_states = input_embeds

        residual, deferred_norm = None, None
        for i in range(len(self.layers)):
            hidden_states, residual, deferred_norm = self.layers[i](
                positions, hidden_states, forward_batch, residual, deferred_norm
            )

        hidden_states, _ = fused_dual_residual_rmsnorm(
            hidden_states,
            residual,
            deferred_norm.weight,
            self.norm.weight,
            deferred_norm.variance_epsilon,
        )

        return hidden_states


class Grok1ForCausalLM(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.quant_config = quant_config

        # Get presharded weights.
        self.load_presharded_mlp = getattr(config, "load_presharded_mlp", False)
        self.load_presharded_moe = (
            getattr(config, "load_presharded_moe", True)
            and self.config.num_local_experts > 0
            and get_tensor_model_parallel_world_size() > 1
        )
        self.load_presharded_attn = getattr(config, "load_presharded_attn", False)
        self.load_presharded_embedding = getattr(
            config, "load_presharded_embedding", False
        )

        self.is_weights_presharded = (
            self.load_presharded_mlp
            or self.load_presharded_moe
            or self.load_presharded_attn
            or self.load_presharded_embedding
        )

        default_replicate_lm_head = False
        self.replicate_lm_head = getattr(
            config, "replicate_lm_head", default_replicate_lm_head
        )

        if self.is_weights_presharded:
            setattr(DefaultModelLoader, "_prepare_weights", _prepare_presharded_weights)

        self.replicate_embedding = getattr(config, "replicate_embedding", False)

        self.model = Grok1Model(
            config,
            quant_config=quant_config,
            load_presharded_moe=self.load_presharded_moe,
            load_presharded_embedding=self.load_presharded_embedding,
            load_presharded_attn=self.load_presharded_attn,
            load_presharded_mlp=self.load_presharded_mlp,
            replicate_embedding=self.replicate_embedding,
            prefix=add_prefix("model", prefix),
        )

        lm_head_params_dtype = None
        if self.replicate_lm_head:
            self.lm_head = ReplicatedLinear(
                config.hidden_size,
                config.vocab_size,
                bias=False,
                params_dtype=lm_head_params_dtype,
                prefix=add_prefix("lm_head", prefix),
            )
            self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.hidden_size,
                use_presharded_weights=self.load_presharded_embedding,
                params_dtype=lm_head_params_dtype,
                prefix=add_prefix("lm_head", prefix),
            )
            self.logits_processor = LogitsProcessor(config)

        self.loaded_param_names = set()

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )

    def load_weights(
        self,
        weights: Iterable[Tuple[str, torch.Tensor]],
        ignore_parent_name: bool = False,
        check_hit_names: bool = True,
        model_config: PretrainedConfig | None = None,
    ) -> dict[str, torch.Tensor]:
        if model_config is None:
            model_config = self.config

        stacked_params_mapping = []
        stacked_params_mapping += [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        stacked_params_mapping += [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        num_experts = model_config.num_local_experts
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="w1",
            ckpt_down_proj_name="w2",
            ckpt_up_proj_name="w3",
            num_experts=num_experts,
        )

        params_dict = dict(self.named_parameters())
        all_names = set(params_dict.keys())
        hit_names = set()

        def load_weight_wrapper(
            name: str, loaded_weight: torch.Tensor, *args, **kwargs
        ):
            # Fuse constant multipliers into the weights
            if "lm_head" in name:
                loaded_weight = (
                    loaded_weight.to(torch.float32)
                    * model_config.output_multiplier_scale
                )

            original_name = name
            if ignore_parent_name:
                name = name.split(".")[-1]

            if name not in params_dict:
                logger.info(f"Skipping {name=} in load_weights_wrapper")
                return

            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            weight_loader(param, loaded_weight, *args, **kwargs)
            hit_names.add(name)
            self.loaded_param_names.add(original_name)

        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                load_weight_wrapper(name, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)

                    load_weight_wrapper(
                        name,
                        loaded_weight,
                        name,
                        shard_id=shard_id,
                        expert_id=expert_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if name is None:
                        continue

                    load_weight_wrapper(name=name, loaded_weight=loaded_weight)

        if check_hit_names:
            if len(hit_names) > 5:
                missing = all_names - hit_names
                missing_exclude_scales = {x for x in missing if "scale" not in x}
                logger.info(
                    f"#all_names: {len(all_names)}, #hit_names: {len(hit_names)}, #missing_exclude_scales: {len(missing_exclude_scales)}",
                )
                if len(missing_exclude_scales) > 0:
                    raise ValueError(
                        f"load_weights failed because some weights are missing: {missing_exclude_scales=}."
                    )

            elif len(hit_names) == 0:
                raise ValueError(
                    f"load_weights failed because it did not hit any names. {all_names=} {hit_names=}"
                )

        return hit_names

    def get_num_params_analytical(self):
        cfg = self.config
        moe_intermediate_size = getattr(
            cfg,
            "moe_intermediate_size",
            getattr(cfg, "intermediate_size", None),
        )
        residual_moe = getattr(cfg, "residual_moe", False)
        if cfg.num_local_experts > 0:
            num_experts = cfg.num_local_experts + (1 if residual_moe else 0)
        else:
            num_experts = 1

        wq = (
            cfg.num_hidden_layers
            * cfg.hidden_size
            * cfg.num_attention_heads
            * cfg.head_dim
        )
        wkv = (
            cfg.num_hidden_layers
            * cfg.hidden_size
            * cfg.num_key_value_heads
            * cfg.head_dim
            * 2
        )
        out = (
            cfg.num_hidden_layers
            * cfg.hidden_size
            * cfg.num_attention_heads
            * cfg.head_dim
        )
        ffn1 = (
            cfg.num_hidden_layers
            * num_experts
            * cfg.hidden_size
            * moe_intermediate_size
            * 2
        )
        ffn2 = (
            cfg.num_hidden_layers
            * num_experts
            * cfg.hidden_size
            * moe_intermediate_size
        )
        embed = cfg.hidden_size * cfg.vocab_size * 2
        return wq + wkv + out + ffn1 + ffn2 + embed

    def get_num_params_torch(self):
        return (
            sum(p.numel() for p in self.parameters())
            * get_tensor_model_parallel_world_size()
        )


old_prepare_weights = getattr(DefaultModelLoader, "_prepare_weights")


def _prepare_presharded_weights(
    self, model_name_or_path: str, revision: Optional[str], fall_back_to_pt: bool
) -> Tuple[str, list[str], bool]:
    import glob
    import os

    if get_tensor_model_parallel_world_size() == 1:
        return old_prepare_weights(self, model_name_or_path, revision, fall_back_to_pt)

    if not os.path.isdir(model_name_or_path):
        from sglang.srt.model_loader.weight_utils import download_weights_from_hf

        allow_patterns = ["*.safetensors", "*.bin"]
        hf_folder = download_weights_from_hf(
            model_name_or_path,
            self.load_config.download_dir,
            allow_patterns,
            revision,
            ignore_patterns=self.load_config.ignore_patterns,
        )
    else:
        hf_folder = model_name_or_path

    tp_rank = get_tensor_model_parallel_rank()

    # The old format
    allow_patterns = [f"*-{tp_rank:03d}.bin"]

    # The new format
    allow_patterns += [f"*-TP-{tp_rank:03d}.safetensors", "*-TP-common.safetensors"]

    hf_weights_files = []
    for pattern in allow_patterns:
        hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))

    if hf_weights_files[0].endswith("safetensors"):
        use_safetensors = True
    else:
        use_safetensors = False

    return hf_folder, hf_weights_files, use_safetensors


class Grok1ModelForCausalLM(Grok1ForCausalLM):
    """An alias for backward-compatbility."""

    pass


EntryClass = [Grok1ForCausalLM, Grok1ModelForCausalLM]
